Abstract: A method of determining a measurement recipe for measurement of in-die targets located within one or more die areas of an exposure field. The method includes obtaining first measurement data relating to measurement of a plurality of reference targets and second measurement data relating to measurement of a plurality of in-die targets, the targets having respective different overlay biases and measured using a plurality of different acquisition settings for acquiring the measurement data. One or more machine learning models are trained using the first measurement data to obtain a plurality of candidate measurement recipes, wherein the candidate measurement recipes include a plurality of combinations of a trained machine learned model and a corresponding acquisition setting; and a preferred measurement recipe is determined from the candidate measurement recipes using the second measurement data.

Abstract: A method of monitoring a semiconductor manufacturing process. The method includes obtaining at least one first trained model being operable to derive local performance parameter data from high resolution metrology data, wherein the local performance parameter data describes a local component, or one or more local contributors thereto, of a performance metric and high resolution metrology data relating to at least one substrate having been subject to at least a part of the semiconductor manufacturing process. Local performance parameter data is determined from the high resolution metrology data using the first trained model. The first trained model is operable to determine the local performance parameter data as if it had been subject to an etch step on at least the immediately prior exposed layer, based on the high resolution metrology data including only metrology data performed prior to any such etch step.